FileGram：基于文件系统行为轨迹的智能体个性化接地方法

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

April 6, 2026

作者: Shuai Liu, Shulin Tian, Kairui Hu, Yuhao Dong, Zhe Yang, Bo Li, Jingkang Yang, Chen Change Loy, Ziwei Liu

cs.AI

摘要

基於本地文件系統的協作式AI代理正迅速成為人機交互的新範式，然而嚴苛的數據限制制約了其個性化效能——嚴格的隱私壁壘與多模態現實行為軌跡的聯合採集難題，阻礙了可擴展的訓練與評估，現有方法仍以交互為中心，忽視了文件系統操作中的密集行為軌跡。為此，我們提出FileGram框架，將代理記憶與個性化錨定於文件系統行為軌跡，其包含三大核心組件：（1）FileGramEngine可擴展的個性驅動數據引擎，能模擬真實工作流並生成細粒度多模態行動序列；（2）FileGramBench基於文件系統行為軌跡的診斷基準，用於評估檔案重建、軌跡解耦、個性漂移檢測及多模態錨定等記憶系統性能；（3）FileGramOS自底向上的記憶架構，直接從原子操作與內容增量構建用戶畫像（而非對話摘要），並通過查詢時抽象將軌跡編碼至程序性、語義性與情境性通道。大量實驗表明，FileGramBench對現有頂尖記憶系統仍具挑戰性，而FileGramEngine與FileGramOS表現卓越。通過開源此框架，我們旨在為個性化記憶中心型文件系統代理的後續研究提供支持。

English

Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real-world traces prevent scalable training and evaluation, and existing methods remain interaction-centric while overlooking dense behavioral traces in file-system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona-driven data engine that simulates realistic workflows and generates fine-grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file-system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom-up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query-time abstraction; extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective, and by open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.